This repository contains resources for the paper "Low-Resource Interlinear Translation: Morphology-Enhanced Neural Models for Ancient Greek" presented at the LoResLM@COLING2025 workshop.
We present a novel approach to interlinear translation from Ancient Greek to English and Polish using morphology-enhanced neural models. Our experiments involved fine-tuning T5-family models in 144 configurations, achieving significant improvements:
- English: 35% BLEU score improvement (44.67 → 60.40)
- Polish: 38% BLEU score improvement (42.92 → 59.33)
- morpht5 - A package including morphology-enhanced T5 model implementations
- Training Code - Scripts used for model training and evaluation
Model performance summaries by target language:
This work is licensed under CC BY-NC-SA 4.0.
@inproceedings{rapacz-smywinski-pohl-2025-low,
title = "Low-Resource Interlinear Translation: Morphology-Enhanced Neural Models for {A}ncient {G}reek",
author = "Rapacz, Maciej and Smywi{\'n}ski-Pohl, Aleksander",
editor = "Hettiarachchi, Hansi and Ranasinghe, Tharindu and Rayson, Paul and Mitkov, Ruslan and Gaber, Mohamed and Premasiri, Damith and Tan, Fiona Anting and Uyangodage, Lasitha",
booktitle = "Proceedings of the First Workshop on Language Models for Low-Resource Languages",
month = jan,
year = "2025",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.loreslm-1.11/",
pages = "145--165",
abstract = "Contemporary machine translation systems prioritize fluent, natural-sounding output with flexible word ordering. In contrast, interlinear translation maintains the source text`s syntactic structure by aligning target language words directly beneath their source counterparts. Despite its importance in classical scholarship, automated approaches to interlinear translation remain understudied. We evaluated neural interlinear translation from Ancient Greek to English and Polish using four transformer-based models: two Ancient Greek-specialized (GreTa and PhilTa) and two general-purpose multilingual models (mT5-base and mT5-large). Our approach introduces novel morphological embedding layers and evaluates text preprocessing and tag set selection across 144 experimental configurations using a word-aligned parallel corpus of the Greek New Testament. Results show that morphological features through dedicated embedding layers significantly enhance translation quality, improving BLEU scores by 35{\%} (44.67 {\textrightarrow} 60.40) for English and 38{\%} (42.92 {\textrightarrow} 59.33) for Polish compared to baseline models. PhilTa achieves state-of-the-art performance for English, while mT5-large does so for Polish. Notably, PhilTa maintains stable performance using only 10{\%} of training data. Our findings challenge the assumption that modern neural architectures cannot benefit from explicit morphological annotations. While preprocessing strategies and tag set selection show minimal impact, the substantial gains from morphological embeddings demonstrate their value in low-resource scenarios."
}